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Spark Administration in Kubernetes

In Fusion 5.0, Spark operates in native Kubernetes mode instead of standalone mode (like in Fusion 4.x). The sections below describe Spark operations in Fusion 5.0.

Node Selectors

You can control which nodes Spark executors are scheduled on using Spark configuration property for a job:

spark.kubernetes.node.selector.<LABEL>=<LABEL_VALUE>

For instance, if a node is labeled with fusion_node_type=spark_only, then you would scheduled Spark executor pods to run on that node using:

spark.kubernetes.node.selector.fusion_node_type=spark_only
Tip
Spark version 2.4.x does not support tolerations for Spark pods; consequently, Spark pods cannot be scheduled on any nodes with taints.

Cluster mode

Fusion 5.0 ships with Spark 2.4.3 and operates in "cluster" mode on top of Kubernetes. In cluster mode, each Spark driver runs in a separate pod and hence resources can be managed per job. Each executor also runs in its own pod.

Spark config defaults

The table below shows the default configurations for Spark. These settings are configured in the job-launcher config map, accessible using kubectl get configmaps <release-name>-job-launcher. Some of these settings are also configurable via Helm.

Table 1. Spark Resource Configurations

Spark Configuration

Default value

Helm Variable

spark.driver.memory

3g

spark.executor.instances

2

executorInstances

spark.executor.memory

3g

spark.executor.cores

6

spark.kubernetes.executor.request.cores

3

Table 2. Spark Kubernetes Configurations

Spark Configuration

Default value

Helm Variable

spark.kubernetes.container.image.pullPolicy

Always

image.imagePullPolicy

spark.kubernetes.container.image.pullSecrets

image.imagePullSecrets

spark.kubernetes.authenticate.driver.serviceAccountName

<name>-job-launcher-spark

spark.kubernetes.driver.container.image

fusion-dev-docker.ci-artifactory.lucidworks.com

image.repository

spark.kubernetes.executor.container.image

fusion-dev-docker.ci-artifactory.lucidworks.com

image.repository